Daniel Racoceanu | Sorbonne University (original) (raw)

Papers by Daniel Racoceanu

In this paper, we propose a generalized solution for lymphocyte detection and segmentation, based... more In this paper, we propose a generalized solution for lymphocyte detection and segmentation, based on a novel image feature extraction method, named exclusive autoencoder (XAE). XAE is compatible with conventional autoencoder (AE) and able to provide additional information about the categorization in the feature space. For the task of lymphocyte detection, XAE was able to reach the an F-score of 99.96%, outperforming the state-of-the-art methods (reporting an F-score of 90%). Further, based on the integration of XAE+FCN (fully connected network) and conventional image processing function blocks provided in CellProfiler, we propose a lymphocyte segmentation pipeline. The obtained Dice coefficient reached 88.31% while the cutting-edge approach was at 74%.

Lecture Notes in Computer Science, 2019

Cerebral aneurysms are among most prevalent and devastating cerebrovascular diseases of adult pop... more Cerebral aneurysms are among most prevalent and devastating cerebrovascular diseases of adult population worldwide. The resulting sequelae of untimely/inadequate therapeutic intervention include subarachnoid hemorrhage. Geometric modeling of aneurysm being the first step in the treatment planning, the scientists therefore focus more on segmentation of aneurysm rather than its detection. A successful aneurysm detection among the bunch of vessels would certainly facilitate and ease the segmentation process. In this work, we present a novel method for aneurysm detection; the key contributions are: contrast enhancement of input image using stochastic resonance concept in wavelet domain, adaptive thresholding, and modified Hough Circle Transform. Experimental results show that the proposed method is efficient in detecting the location and type of aneurysm.

European Urology Supplements, 2019

Introduction & Objectives: In 2017, prostate cancer (PCa) was the second most common cancer in me... more Introduction & Objectives: In 2017, prostate cancer (PCa) was the second most common cancer in men after lung cancer. While there are different courses of action to treat the disease, its mortality in Peru is higher than 50%. Conventionally, PCa is diagnosed by evaluating tissue biopsies, and classified according to the Gleason grading system. Novel molecular classifications of PCa have been proposed for diagnostic and prognostic purposes. The main goal of this work is to implement a tool predicting the disease free time of patient according to the genomic expression and highlight the genes playing an influential role on the prediction. Materials & Methods: Modern techniques to classify data keep getting broader and more accurate, in particular with the introduction of Neural Networks(NN). We implement an Artificial Neural Network automatic genomic classification strategy based on a Local Interpretable Model-Agnostic Explanations (LIME) algorithm because it allows the network to choose the features of major discriminative significance. As a proof-of-concept, we selected a subset of 242 genes related to recurrence from 499 PCa genomes to build the neural networks. Results: Instead of using a classic fully connected layer, we implemented an Artificial Neural Network where the final network provides the predicted survival rate or time to recurrence. The resulting neural network can predict the time of recurrence within a range of three months based on the genomic expression with an accuracy of 96,9% and a loss of less than 5%. Using the implemented LIME algorithm, our results indicate that this subset of genes is informative of recurrence and plays a substantial role in the prediction. Conclusions: Instead of using a classic fully connected layer, we implemented an Artificial Neural Network where the final network provides the predicted survival rate or time to recurrence. The resulting neural network can predict the time of recurrence within a range of three months based on the genomic expression with an accuracy of 96,9% and a loss of less than 5%. Using the implemented LIME algorithm, our results indicate that this subset of genes is informative of recurrence and plays a substantial role in the prediction.

Virchows Archiv The …

... Formalism Adina TUTAC, Daniel RACOCEANU, Nicolas LOMENIE, Wee-Kheng LEOW, Ludovic ROUX, Vladi... more ... Formalism Adina TUTAC, Daniel RACOCEANU, Nicolas LOMENIE, Wee-Kheng LEOW, Ludovic ROUX, Vladimir CRETU, Thomas PUTTI IPAL - Image Perception, Access & Language International Research Unit, Singapore http://ipal.i2r.a-star.edu.sg/ ...

Diagnostics, 2021

The interest in implementing digital pathology (DP) workflows to obtain whole slide image (WSI) f... more The interest in implementing digital pathology (DP) workflows to obtain whole slide image (WSI) files for diagnostic purposes has increased in the last few years. The increasing performance of technical components and the Food and Drug Administration (FDA) approval of systems for primary diagnosis led to increased interest in applying DP workflows. However, despite this revolutionary transition, real world data suggest that a fully digital approach to the histological workflow has been implemented in only a minority of pathology laboratories. The objective of this study is to facilitate the implementation of DP workflows in pathology laboratories, helping those involved in this process of transformation to identify: (a) the scope and the boundaries of the DP transformation; (b) how to introduce automation to reduce errors; (c) how to introduce appropriate quality control to guarantee the safety of the process and (d) the hardware and software needed to implement DP systems inside th...

IEEE Reviews in Biomedical Engineering, 2014

13th International Conference on Medical Information Processing and Analysis, 2017

The fundamental role of vascular supply in tumor growth makes the evaluation of the angiogenesis ... more The fundamental role of vascular supply in tumor growth makes the evaluation of the angiogenesis crucial in assessing effect of anti-angiogenic therapies. Since many years, such therapies are designed to inhibit the vascular endothelial growth factor (VEGF). To contribute to the assessment of anti-angiogenic agent (Pazopanib) effect on vascular and cellular structures, we acquired data from tumors extracted from a murine tumor model using Multi-Fluorescence Scanning. In this paper, we implemented an unsupervised algorithm combining the Watershed segmentation and Markov Random Field model (MRF). This algorithm allowed us to quantify the proportion of apoptotic endothelial cells and to generate maps according to cell density. Stronger association between apoptosis and endothelial cells was revealed in the tumors receiving anti-angiogenic therapy (n = 4) as compared to those receiving placebo (n = 4). The percentage of apoptotic cells on tumor area is mostly endothelial. Lower density cells were detected in tumor slices presenting higher apoptotic endothelial areas.

Prostate cancer (PCa) is one of the most common cancers in men, being also the second most deadly... more Prostate cancer (PCa) is one of the most common cancers in men, being also the second most deadly cancer after lung cancer. There is increasing interest in active surveillance and minimally invasive focal therapies in PCa to avoid morbidities associated with whole gland therapy. Tumor volume represents an essential prognostic factor of PCa and the definition of index lesion volume is critical for appropriate decision making, especially for image guide focal treatment or in case of active surveillance. Multi-parametric Magnetic Resonance Imaging (mp-MRI) is the modality of choice for the detection and the localization of PCa foci. However, little has been published on mp-MRI accuracy in determining PCa volume, especially at 3T. There is insufficient evidence and no consensus to determine which of the methods for measuring volume is optimal. The objective of this study concerns the elaboration of an algorithm for automatic interpretation of mp-MRI. We determine the accuracy of the pro...

Diagnostic Pathology, 2016

Background Recently, anatomic pathology (AP) has seen the introduction of several tools such as s... more Background Recently, anatomic pathology (AP) has seen the introduction of several tools such as slide scanners and virtual slide technologies, creating the conditions for broader adoption of computer aided diagnosis based on whole slide images (WSI). This change brings up a number of new scientific challenges such as the sustainable management of the explicit and unambiguous semantics associated to the diagnostic interpretation of AP images by both humans (pathologists) and computers (image analysis algorithms) . In order to reduce inter-observer variability between AP reports of malignant tumors, the College of American Pathologists edited more than 60 organ-specific Cancer Checklists and associated Protocols (CAP-CC&P). Each checklist includes a set of AP observations that are expected to be reported by pathologists in organ-specific AP cancer reports. Our objective was to i) identify the available histopathological formalized knowledge from NCBO Bioportal and UMLS metathesaurus i...

Exploring the spatial interactions between tumor and the inflammatory microenvironment using digi... more Exploring the spatial interactions between tumor and the inflammatory microenvironment using digital pathology image analysis can contribute to a better understanding of the immune function and tumor heterogeneity. We address this by providing tools able to reveal various metrics describing spatial relationships in the cancer ecosystem. The approach comprises nuclei segmentation and classification, using supervised learning algorithm, to detect lymphoid aggregates and tumor patterns, and spatial distribution quantification using sparse sets' mathematical morphology. Tumor patterns were classified into three groups: surrounded by lymphocytes, close to lymphoid aggregates or distant and might be protected from immune attack. The approach provides statistical assessment and comprehensive visual representation of the inflammatory tumor microenvironment.

Medical image analysis, Jan 3, 2016

Colorectal adenocarcinoma originating in intestinal glandular structures is the most common form ... more Colorectal adenocarcinoma originating in intestinal glandular structures is the most common form of colon cancer. In clinical practice, the morphology of intestinal glands, including architectural appearance and glandular formation, is used by pathologists to inform prognosis and plan the treatment of individual patients. However, achieving good inter-observer as well as intra-observer reproducibility of cancer grading is still a major challenge in modern pathology. An automated approach which quantifies the morphology of glands is a solution to the problem. This paper provides an overview to the Gland Segmentation in Colon Histology Images Challenge Contest (GlaS) held at MICCAI'2015. Details of the challenge, including organization, dataset and evaluation criteria, are presented, along with the method descriptions and evaluation results from the top performing methods.

Pathobiology : journal of immunopathology, molecular and cellular biology, 2016

Being able to provide a traceable and dynamic second opinion has become an ethical priority for p... more Being able to provide a traceable and dynamic second opinion has become an ethical priority for patients and health care professionals in modern computer-aided medicine. In this perspective, a semantic cognitive virtual microscopy approach has been recently initiated, the MICO project, by focusing on cognitive digital pathology. This approach supports the elaboration of pathology-compliant daily protocols dedicated to breast cancer grading, in particular mitotic counts and nuclear atypia. A proof of concept has thus been elaborated, and an extension of these approaches is now underway in a collaborative digital pathology framework, the FlexMIm project. As important milestones on the way to routine digital pathology, a series of pioneer international benchmarking initiatives have been launched for mitosis detection (MITOS), nuclear atypia grading (MITOS-ATYPIA) and glandular structure detection (GlaS), some of the fundamental grading components in diagnosis and prognosis. These initi...

IFAC Proceedings Volumes, 2005

IEEE transactions on medical imaging, Jun 6, 2016

In this paper we present a pipeline for automatic analysis of neuronal morphology: from detection... more In this paper we present a pipeline for automatic analysis of neuronal morphology: from detection, modeling to digital reconstruction. First, we present an automatic, unsupervised object detection framework using stochastic marked point process. It extracts connected neuronal networks by fitting special configuration of marked objects to the centreline of the neurite branches in the image volume giving us position, local width and orientation information. Semantic modeling of neuronal morphology in terms of critical nodes like bifurcations and terminals, generates various geometric and morphology descriptors such as branching index, branching angles, total neurite length, internodal lengths for statistical inference on characteristic neuronal features. From the detected branches we reconstruct neuronal tree morphology using robust and efficient numerical fast marching methods. We capture a mathematical model abstracting out the relevant position, shape and connectivity information a...

Digital pathology represents one of the major and challenging evolutions in modern medicine. Path... more Digital pathology represents one of the major and challenging evolutions in modern medicine. Pathological exams constitute not only the gold standard in most of medical protocols, but also play a critical and legal role in the diagnosis process. Diagnosing a disease after manually analyzing numerous biopsy slides represents a labor-intensive work for pathologists. Thanks to the recent advances in digital histopathology, the recognition of histological tissue patterns in a high-content Whole Slide Image (WSI) has the potential to provide valuable assistance to the pathologist in his daily practice. Histopathological classification and grading of biopsy samples provide valuable prognostic information that could be used for diagnosis and treatment support. Nottingham grading system is the standard for breast cancer grading. It combines three criteria, namely tubule formation (also referenced as glandular architecture), nuclear atypia and mitosis count. Manual detection and counting of ...

... References [1] O. Steichen, C. Daniel - Le Bozec, M. Thieu, E. Zapletal and MC Jaulent, “Comp... more ... References [1] O. Steichen, C. Daniel - Le Bozec, M. Thieu, E. Zapletal and MC Jaulent, “Computation of semantic similarity within an ontology of breast pathology to assist inter-observer consensus,” Computers in ... [16] AE Tutac, D. Racoceanu, T. Putti, W. Xiong, WK Leow and V ...

2017 IEEE International Ultrasonics Symposium (IUS), 2017

Previous studies have shown that quantitative ultrasound (QUS) methods can provide tissue-microst... more Previous studies have shown that quantitative ultrasound (QUS) methods can provide tissue-microstructure information and are able to successfully detect metastases in human lymph nodes (LNs) harvested from cancer patients. Nevertheless, the gold standard for diagnosis remains pathological evaluation of histology photomicrographs. The goal of the present study is to compare QUS-based and histology-based features which proved to be most valuable for metastatic classification in LNs.

Medical Imaging 2016: Digital Pathology, 2016

The morphology of intestinal glands is an important and significant indicator of the level of the... more The morphology of intestinal glands is an important and significant indicator of the level of the severity of an inflammatory bowel disease, and has also been used routinely by pathologists to evaluate the malignancy and the prognosis of colorectal cancers such as adenocarcinomas. The extraction of meaningful information describing the morphology of glands relies on an accurate segmentation method. In this work, we propose a novel technique based on mathematical morphology that characterizes the spatial positioning of nuclei for intestinal gland segmentation in histopathological images. According to their appearance, glands can be divided into two types: hallow glands and solid glands. Hallow glands are composed of lumen and/or goblet cells cytoplasm, or filled with abscess in some advanced stages of the disease, while solid glands are composed of bunches of cells clustered together and can also be filled with necrotic debris. Given this scheme, an efficient characterization of the spatial distribution of cells is sufficient to carry out the segmentation. In this approach, hallow glands are first identified as regions empty of nuclei and surrounded by thick layers of epithelial cells, then solid glands are identified by detecting regions crowded of people. First, cell nuclei are identified by color classification. Then, morphological maps are generated by the mean of advanced morphological operators applied to nuclei objects in order to interpret their spatial distribution and properties to identify candidates for glands central-regions and epithelial layers, that are combined to extract the glandular structures.

— Histopathological classification and grading of biopsy specimens play an important role in earl... more — Histopathological classification and grading of biopsy specimens play an important role in early cancer detection and prognosis. Nottingham scoring system is one of the standard grading procedures used in breast cancer assessment, where three parameters, Mitotic Count (MC), Nuclear Pleomorphism (NP), and Tubule Formation (TF) are used for prognostic information. The grading takes into account the deviations in cellular structures and appearance from normal, using measures such as density, size, colour and regularity. Cell structures in tissue images are also known to exhibit multifractal characteristics. This paper looks at the multifractal properties of several graded biopsy specimens and analyses the dependency and variation of the fractal parameters with respect to the scores assigned by pathologists. Keywords-Breast cancer assessment; Multifractal spectra; Image analysis; Histopathological classification; Feature detection; Cancer grading

Towards semantic-driven high-content image analysis: An operational instantiation for mitosis det... more Towards semantic-driven high-content image analysis: An operational instantiation for mitosis detection in digital histopathology

In this paper, we propose a generalized solution for lymphocyte detection and segmentation, based... more In this paper, we propose a generalized solution for lymphocyte detection and segmentation, based on a novel image feature extraction method, named exclusive autoencoder (XAE). XAE is compatible with conventional autoencoder (AE) and able to provide additional information about the categorization in the feature space. For the task of lymphocyte detection, XAE was able to reach the an F-score of 99.96%, outperforming the state-of-the-art methods (reporting an F-score of 90%). Further, based on the integration of XAE+FCN (fully connected network) and conventional image processing function blocks provided in CellProfiler, we propose a lymphocyte segmentation pipeline. The obtained Dice coefficient reached 88.31% while the cutting-edge approach was at 74%.

Lecture Notes in Computer Science, 2019

Cerebral aneurysms are among most prevalent and devastating cerebrovascular diseases of adult pop... more Cerebral aneurysms are among most prevalent and devastating cerebrovascular diseases of adult population worldwide. The resulting sequelae of untimely/inadequate therapeutic intervention include subarachnoid hemorrhage. Geometric modeling of aneurysm being the first step in the treatment planning, the scientists therefore focus more on segmentation of aneurysm rather than its detection. A successful aneurysm detection among the bunch of vessels would certainly facilitate and ease the segmentation process. In this work, we present a novel method for aneurysm detection; the key contributions are: contrast enhancement of input image using stochastic resonance concept in wavelet domain, adaptive thresholding, and modified Hough Circle Transform. Experimental results show that the proposed method is efficient in detecting the location and type of aneurysm.

European Urology Supplements, 2019

Introduction & Objectives: In 2017, prostate cancer (PCa) was the second most common cancer in me... more Introduction & Objectives: In 2017, prostate cancer (PCa) was the second most common cancer in men after lung cancer. While there are different courses of action to treat the disease, its mortality in Peru is higher than 50%. Conventionally, PCa is diagnosed by evaluating tissue biopsies, and classified according to the Gleason grading system. Novel molecular classifications of PCa have been proposed for diagnostic and prognostic purposes. The main goal of this work is to implement a tool predicting the disease free time of patient according to the genomic expression and highlight the genes playing an influential role on the prediction. Materials & Methods: Modern techniques to classify data keep getting broader and more accurate, in particular with the introduction of Neural Networks(NN). We implement an Artificial Neural Network automatic genomic classification strategy based on a Local Interpretable Model-Agnostic Explanations (LIME) algorithm because it allows the network to choose the features of major discriminative significance. As a proof-of-concept, we selected a subset of 242 genes related to recurrence from 499 PCa genomes to build the neural networks. Results: Instead of using a classic fully connected layer, we implemented an Artificial Neural Network where the final network provides the predicted survival rate or time to recurrence. The resulting neural network can predict the time of recurrence within a range of three months based on the genomic expression with an accuracy of 96,9% and a loss of less than 5%. Using the implemented LIME algorithm, our results indicate that this subset of genes is informative of recurrence and plays a substantial role in the prediction. Conclusions: Instead of using a classic fully connected layer, we implemented an Artificial Neural Network where the final network provides the predicted survival rate or time to recurrence. The resulting neural network can predict the time of recurrence within a range of three months based on the genomic expression with an accuracy of 96,9% and a loss of less than 5%. Using the implemented LIME algorithm, our results indicate that this subset of genes is informative of recurrence and plays a substantial role in the prediction.

Virchows Archiv The …

... Formalism Adina TUTAC, Daniel RACOCEANU, Nicolas LOMENIE, Wee-Kheng LEOW, Ludovic ROUX, Vladi... more ... Formalism Adina TUTAC, Daniel RACOCEANU, Nicolas LOMENIE, Wee-Kheng LEOW, Ludovic ROUX, Vladimir CRETU, Thomas PUTTI IPAL - Image Perception, Access & Language International Research Unit, Singapore http://ipal.i2r.a-star.edu.sg/ ...

Diagnostics, 2021

The interest in implementing digital pathology (DP) workflows to obtain whole slide image (WSI) f... more The interest in implementing digital pathology (DP) workflows to obtain whole slide image (WSI) files for diagnostic purposes has increased in the last few years. The increasing performance of technical components and the Food and Drug Administration (FDA) approval of systems for primary diagnosis led to increased interest in applying DP workflows. However, despite this revolutionary transition, real world data suggest that a fully digital approach to the histological workflow has been implemented in only a minority of pathology laboratories. The objective of this study is to facilitate the implementation of DP workflows in pathology laboratories, helping those involved in this process of transformation to identify: (a) the scope and the boundaries of the DP transformation; (b) how to introduce automation to reduce errors; (c) how to introduce appropriate quality control to guarantee the safety of the process and (d) the hardware and software needed to implement DP systems inside th...

IEEE Reviews in Biomedical Engineering, 2014

13th International Conference on Medical Information Processing and Analysis, 2017

The fundamental role of vascular supply in tumor growth makes the evaluation of the angiogenesis ... more The fundamental role of vascular supply in tumor growth makes the evaluation of the angiogenesis crucial in assessing effect of anti-angiogenic therapies. Since many years, such therapies are designed to inhibit the vascular endothelial growth factor (VEGF). To contribute to the assessment of anti-angiogenic agent (Pazopanib) effect on vascular and cellular structures, we acquired data from tumors extracted from a murine tumor model using Multi-Fluorescence Scanning. In this paper, we implemented an unsupervised algorithm combining the Watershed segmentation and Markov Random Field model (MRF). This algorithm allowed us to quantify the proportion of apoptotic endothelial cells and to generate maps according to cell density. Stronger association between apoptosis and endothelial cells was revealed in the tumors receiving anti-angiogenic therapy (n = 4) as compared to those receiving placebo (n = 4). The percentage of apoptotic cells on tumor area is mostly endothelial. Lower density cells were detected in tumor slices presenting higher apoptotic endothelial areas.

Prostate cancer (PCa) is one of the most common cancers in men, being also the second most deadly... more Prostate cancer (PCa) is one of the most common cancers in men, being also the second most deadly cancer after lung cancer. There is increasing interest in active surveillance and minimally invasive focal therapies in PCa to avoid morbidities associated with whole gland therapy. Tumor volume represents an essential prognostic factor of PCa and the definition of index lesion volume is critical for appropriate decision making, especially for image guide focal treatment or in case of active surveillance. Multi-parametric Magnetic Resonance Imaging (mp-MRI) is the modality of choice for the detection and the localization of PCa foci. However, little has been published on mp-MRI accuracy in determining PCa volume, especially at 3T. There is insufficient evidence and no consensus to determine which of the methods for measuring volume is optimal. The objective of this study concerns the elaboration of an algorithm for automatic interpretation of mp-MRI. We determine the accuracy of the pro...

Diagnostic Pathology, 2016

Background Recently, anatomic pathology (AP) has seen the introduction of several tools such as s... more Background Recently, anatomic pathology (AP) has seen the introduction of several tools such as slide scanners and virtual slide technologies, creating the conditions for broader adoption of computer aided diagnosis based on whole slide images (WSI). This change brings up a number of new scientific challenges such as the sustainable management of the explicit and unambiguous semantics associated to the diagnostic interpretation of AP images by both humans (pathologists) and computers (image analysis algorithms) . In order to reduce inter-observer variability between AP reports of malignant tumors, the College of American Pathologists edited more than 60 organ-specific Cancer Checklists and associated Protocols (CAP-CC&P). Each checklist includes a set of AP observations that are expected to be reported by pathologists in organ-specific AP cancer reports. Our objective was to i) identify the available histopathological formalized knowledge from NCBO Bioportal and UMLS metathesaurus i...

Exploring the spatial interactions between tumor and the inflammatory microenvironment using digi... more Exploring the spatial interactions between tumor and the inflammatory microenvironment using digital pathology image analysis can contribute to a better understanding of the immune function and tumor heterogeneity. We address this by providing tools able to reveal various metrics describing spatial relationships in the cancer ecosystem. The approach comprises nuclei segmentation and classification, using supervised learning algorithm, to detect lymphoid aggregates and tumor patterns, and spatial distribution quantification using sparse sets' mathematical morphology. Tumor patterns were classified into three groups: surrounded by lymphocytes, close to lymphoid aggregates or distant and might be protected from immune attack. The approach provides statistical assessment and comprehensive visual representation of the inflammatory tumor microenvironment.

Medical image analysis, Jan 3, 2016

Colorectal adenocarcinoma originating in intestinal glandular structures is the most common form ... more Colorectal adenocarcinoma originating in intestinal glandular structures is the most common form of colon cancer. In clinical practice, the morphology of intestinal glands, including architectural appearance and glandular formation, is used by pathologists to inform prognosis and plan the treatment of individual patients. However, achieving good inter-observer as well as intra-observer reproducibility of cancer grading is still a major challenge in modern pathology. An automated approach which quantifies the morphology of glands is a solution to the problem. This paper provides an overview to the Gland Segmentation in Colon Histology Images Challenge Contest (GlaS) held at MICCAI'2015. Details of the challenge, including organization, dataset and evaluation criteria, are presented, along with the method descriptions and evaluation results from the top performing methods.

Pathobiology : journal of immunopathology, molecular and cellular biology, 2016

Being able to provide a traceable and dynamic second opinion has become an ethical priority for p... more Being able to provide a traceable and dynamic second opinion has become an ethical priority for patients and health care professionals in modern computer-aided medicine. In this perspective, a semantic cognitive virtual microscopy approach has been recently initiated, the MICO project, by focusing on cognitive digital pathology. This approach supports the elaboration of pathology-compliant daily protocols dedicated to breast cancer grading, in particular mitotic counts and nuclear atypia. A proof of concept has thus been elaborated, and an extension of these approaches is now underway in a collaborative digital pathology framework, the FlexMIm project. As important milestones on the way to routine digital pathology, a series of pioneer international benchmarking initiatives have been launched for mitosis detection (MITOS), nuclear atypia grading (MITOS-ATYPIA) and glandular structure detection (GlaS), some of the fundamental grading components in diagnosis and prognosis. These initi...

IFAC Proceedings Volumes, 2005

IEEE transactions on medical imaging, Jun 6, 2016

In this paper we present a pipeline for automatic analysis of neuronal morphology: from detection... more In this paper we present a pipeline for automatic analysis of neuronal morphology: from detection, modeling to digital reconstruction. First, we present an automatic, unsupervised object detection framework using stochastic marked point process. It extracts connected neuronal networks by fitting special configuration of marked objects to the centreline of the neurite branches in the image volume giving us position, local width and orientation information. Semantic modeling of neuronal morphology in terms of critical nodes like bifurcations and terminals, generates various geometric and morphology descriptors such as branching index, branching angles, total neurite length, internodal lengths for statistical inference on characteristic neuronal features. From the detected branches we reconstruct neuronal tree morphology using robust and efficient numerical fast marching methods. We capture a mathematical model abstracting out the relevant position, shape and connectivity information a...

Digital pathology represents one of the major and challenging evolutions in modern medicine. Path... more Digital pathology represents one of the major and challenging evolutions in modern medicine. Pathological exams constitute not only the gold standard in most of medical protocols, but also play a critical and legal role in the diagnosis process. Diagnosing a disease after manually analyzing numerous biopsy slides represents a labor-intensive work for pathologists. Thanks to the recent advances in digital histopathology, the recognition of histological tissue patterns in a high-content Whole Slide Image (WSI) has the potential to provide valuable assistance to the pathologist in his daily practice. Histopathological classification and grading of biopsy samples provide valuable prognostic information that could be used for diagnosis and treatment support. Nottingham grading system is the standard for breast cancer grading. It combines three criteria, namely tubule formation (also referenced as glandular architecture), nuclear atypia and mitosis count. Manual detection and counting of ...

... References [1] O. Steichen, C. Daniel - Le Bozec, M. Thieu, E. Zapletal and MC Jaulent, “Comp... more ... References [1] O. Steichen, C. Daniel - Le Bozec, M. Thieu, E. Zapletal and MC Jaulent, “Computation of semantic similarity within an ontology of breast pathology to assist inter-observer consensus,” Computers in ... [16] AE Tutac, D. Racoceanu, T. Putti, W. Xiong, WK Leow and V ...

2017 IEEE International Ultrasonics Symposium (IUS), 2017

Previous studies have shown that quantitative ultrasound (QUS) methods can provide tissue-microst... more Previous studies have shown that quantitative ultrasound (QUS) methods can provide tissue-microstructure information and are able to successfully detect metastases in human lymph nodes (LNs) harvested from cancer patients. Nevertheless, the gold standard for diagnosis remains pathological evaluation of histology photomicrographs. The goal of the present study is to compare QUS-based and histology-based features which proved to be most valuable for metastatic classification in LNs.

Medical Imaging 2016: Digital Pathology, 2016

The morphology of intestinal glands is an important and significant indicator of the level of the... more The morphology of intestinal glands is an important and significant indicator of the level of the severity of an inflammatory bowel disease, and has also been used routinely by pathologists to evaluate the malignancy and the prognosis of colorectal cancers such as adenocarcinomas. The extraction of meaningful information describing the morphology of glands relies on an accurate segmentation method. In this work, we propose a novel technique based on mathematical morphology that characterizes the spatial positioning of nuclei for intestinal gland segmentation in histopathological images. According to their appearance, glands can be divided into two types: hallow glands and solid glands. Hallow glands are composed of lumen and/or goblet cells cytoplasm, or filled with abscess in some advanced stages of the disease, while solid glands are composed of bunches of cells clustered together and can also be filled with necrotic debris. Given this scheme, an efficient characterization of the spatial distribution of cells is sufficient to carry out the segmentation. In this approach, hallow glands are first identified as regions empty of nuclei and surrounded by thick layers of epithelial cells, then solid glands are identified by detecting regions crowded of people. First, cell nuclei are identified by color classification. Then, morphological maps are generated by the mean of advanced morphological operators applied to nuclei objects in order to interpret their spatial distribution and properties to identify candidates for glands central-regions and epithelial layers, that are combined to extract the glandular structures.

— Histopathological classification and grading of biopsy specimens play an important role in earl... more — Histopathological classification and grading of biopsy specimens play an important role in early cancer detection and prognosis. Nottingham scoring system is one of the standard grading procedures used in breast cancer assessment, where three parameters, Mitotic Count (MC), Nuclear Pleomorphism (NP), and Tubule Formation (TF) are used for prognostic information. The grading takes into account the deviations in cellular structures and appearance from normal, using measures such as density, size, colour and regularity. Cell structures in tissue images are also known to exhibit multifractal characteristics. This paper looks at the multifractal properties of several graded biopsy specimens and analyses the dependency and variation of the fractal parameters with respect to the scores assigned by pathologists. Keywords-Breast cancer assessment; Multifractal spectra; Image analysis; Histopathological classification; Feature detection; Cancer grading

Towards semantic-driven high-content image analysis: An operational instantiation for mitosis det... more Towards semantic-driven high-content image analysis: An operational instantiation for mitosis detection in digital histopathology

Objective : Semantic based breast cancer grading support Context: Histopathological examination i... more Objective : Semantic based breast cancer grading support Context: Histopathological examination is a powerful method for prognosis of major diseases such as breast cancer. Nevertheless, nowadays, the analysis of the biopsy slides and related microscopic images largely remains the work of human experts. Contribution : The cognitive virtual microscopic framework, through an extended modeling and use of medical knowledge, has the capacity to analyse histopathological images and to perform grading of breast cancer, providing pathologists with robust and traceable second opinion.